Organ dose estimation for retrospective epidemiological studies of late effects in radiotherapy patients involves two challenges: radiological images to represent patient anatomy are not usually available for patient cohorts who were treated years ago, and efficient dose reconstruction methods for large-scale patient cohorts are not well established. In the current study, we developed methods to reconstruct organ doses for radiotherapy patients by using a series of computational human phantoms coupled with a commercial treatment planning system (TPS) and a radiotherapy-dedicated Monte Carlo transport code, and performed illustrative dose calculations. First, we developed methods to convert the anatomy and organ contours of the pediatric and adult hybrid computational phantom series to Digital Imaging and Communications in Medicine (DICOM)-image and DICOM-structure files, respectively. The resulting DICOM files were imported to a commercial TPS for simulating radiotherapy and dose calculation for in-field organs. The conversion process was validated by comparing electron densities relative to water and organ volumes between the hybrid phantoms and the DICOM files imported in TPS, which showed agreements within 0.1% and 2%, respectively. Second, we developed a procedure to transfer DICOM-RT files generated from the Eclipse system directly to a Monte Carlo transport code, X-ray Voxel Monte Carlo (XVMC) for more accurate dose calculations. Third, to illustrate the performance of the established methods, we simulated a whole brain treatment for the 10-year-old male phantom and a prostate treatment for the adult male phantom. Radiation doses to selected organs were calculated using the Eclipse and XVMC, and compared to each other. Organ average doses from the two methods matched within 7%, whereas maximum and minimum point doses differed up to 45%. The dosimetry methods and procedures established in this study will be useful for the reconstruction of organ dose to support retrospective epidemiological studies of late effects in radiotherapy patients.

A Histogram Analysis in Radiation Therapy (HART) program was primarily developed to increase the efficiency and accuracy of dose–volume histogram (DVH) analysis of large quantities of patient data in radiation therapy research. The program was written in MATLAB to analyze patient plans exported from the treatment planning system false(Pinnacle3false) in the American Association of Physicists in Medicine/Radiation Therapy Oncology Group (AAPM/RTOG) format. HART‐computed DVH data was validated against manually extracted data from the planning system for five head and neck cancer patients treated with the intensity‐modulated radiation therapy (IMRT) technique. HART calculated over 4000 parameters from the differential DVH (dDVH) curves for each patient in approximately 10–15 minutes. Manual extraction of this amount of data required 5 to 6 hours. The normalized root mean square deviation (NRMSD) for the HART–extracted DVH outcomes was less than 1%, or within 0.5% distance‐to‐agreement (DTA). This tool is supported with various user‐friendly options and graphical displays. Additional features include optimal polynomial modeling of DVH curves for organs, treatment plan indices (TPI) evaluation, plan‐specific outcome analysis (POA), and spatial DVH (zDVH) and dose surface histogram (DSH) analyses, respectively. HART is freely available to the radiation oncology community.PACS numbers: 87.53.‐j; 87.53.Tf; 87.53.Xd.

The authors compared the relative dosimetric merits of Gamma Knife (GK) and CyberKnife (CK) in 15 patients with 26 brain metastases. All patients were initially treated with the Leksell GK 4C. The same patients were used to generate comparative CK treatment plans. The tissue volume receiving more than 12 Gy (V12), the difference between V12 and tumor volume (V12net), homogeneity index (HI), and gradient indices (GI25, GI50) were calculated. Peripheral dose falloff and three conformity indices were compared. The median tumor volume was 2.50 cm3 (range, 0. 044‐19.9). A median dose of 18 Gy (range, 15‐22) was prescribed. In GK and CK plans, doses were prescribed to the 40‐50% and 77‐92% isodose lines, respectively. Comparing GK to CK, the respective parametric values false(median±standard deviationfalse) were: minimum dose (18.2±3.4 vs. 17.6±2.4 Gy, p=0.395); mean dose (29.6±5.1 vs.20.6±2.8 Gy, p<0.00001); maximum dose (40.3±6.5 vs.22.7±3.3 Gy, p<0.00001); and HI (2.22±0.19 vs. 1.18±0.06, p<0.00001). The median dosimetric indices (GK vs. CK, with range) were: RTOG_CI, 1.76 (1.12‐4.14) vs. 1.53 (1.16‐2.12), p=0.0220; CI, 1.76 (1.15‐4.14) vs. 1.55 (1.18‐2.21), p=0.050; nCI, 1.76 (1.59‐4.14) vs. 1.57 (1.20‐2.30), p=0.082; GI50, 2.91 (2.48‐3.67) vs. 4.90 (3.42‐11.68), p<0.00001; GI25, 6.58 (4.18‐10.20) vs. 14.85 (8.80‐48.37), p<0.00001. Average volume ratio (AVR) differences favored GK at multiple normalized isodose levels false(p<0.00001false). We concluded that in patients with brain metastases, CK and GK resulted in dosimetrically comparable plans that were nearly equivalent in several metrics, including target coverage and minimum dose within the target. Compared to GK, CK produced more homogenous plans with significantly lower mean and maximum doses, and achieved more conformal plans by RTOG_CI criteria. By GI and AVR analyses, GK plans had sharper peripheral dose falloff in most cases.PACS number: 89.20.‐a

Radiotherapy treatment planning systems are designed for the fast calculation of dose to the tumor bed and nearby organs at risk using x-ray computed tomography (CT) images. However, CT images for a patient are typically available for only a small portion of the body, and in some cases, such as for retrospective epidemiological studies, no images may be available at all. When dose to organs that lie out-of-scan must be estimated, a convenient alternative for the unknown patient anatomy is to use a matching whole-body computational phantom as a surrogate. The purpose of the current work is to connect such computational phantoms to commercial radiotherapy treatment planning systems for retrospective organ dose estimation. A custom software with graphical user interface, called the DICOM-RT Generator, was developed in MATLAB to convert voxel computational phantoms into the Digital Imaging and Communications in Medicine radiotherapy (DICOM-RT) format, compatible with commercial treatment planning systems. DICOM CT image sets for the phantoms are created via a density-to-Hounsfield unit conversion curve. Accompanying structure sets containing the organ contours are automatically generated by tracing binary masks of user-specified organs on each phantom CT slice. The software was tested on a library of body size-dependent phantoms, the International Commission on Radiological Protection reference phantoms, and a canine voxel phantom, taking only a few minutes per conversion. The resulting DICOM-RT files were tested on several commercial treatment planning systems. As an example application, a library of converted phantoms was used to estimate organ doses for members of the National Wilms Tumor Study cohort. The converted phantom library, in DICOM format, and a standalone MATLAB-compiled executable of the DICOM-RT Generator are available for others to use for research purposes (http://ncidose.cancer.gov).

IntroductionThe gamma analysis used for quality assurance of a complex radiotherapy plan examines the dosimetric equivalence between planned and measured dose distributions within some tolerance. This study explores whether the dosimetric difference is correlated with any radiobiological difference between delivered and planned dose.Methods VMAT or IMRT plans optimized for 14 cancer patients were calculated and delivered to a QA device. Measured dose was compared against planned dose using 2‐D gamma analysis. Dose volume histograms (for various patient structures) obtained by interpolating measured data were compared against the planned ones using a 3‐D gamma analysis. Dose volume histograms were used in the Poisson model to calculate tumor control probability for the treatment targets and in the Sigmoid dose–response model to calculate normal tissue complication probability for the organs at risk.ResultsDifferences in measured and planned dosimetric data for the patient plans passing at ≥94.9% rate at 3%/3 mm criteria are not statistically significant. Average ± standard deviation tumor control probabilities based on measured and planned data are 65.8±4.0% and 67.8±4.1% for head and neck, and 71.9±2.7% and 73.3±3.1% for lung plans, respectively. The differences in tumor control probabilities obtained from measured and planned dose are statistically insignificant. However, the differences in normal tissue complication probabilities for larynx, lungs‐GTV, heart, and cord are statistically significant for the patient plans meeting ≥94.9% passing criterion at 3%/3 mm.ConclusionA ≥90% gamma passing criterion at 3%/3 mm cannot assure the radiobiological equivalence between planned and delivered dose. These results agree with the published literature demonstrating the inadequacy of the criterion for dosimetric QA and suggest for a tighter tolerance.

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In clinical practice, evaluation of clinical efficacy of treatment planning stems from the radiation oncologist's experience in accurately targeting tumors, while keeping minimal toxicity to various organs at risk (OAR) involved. A more objective, quantitative method may be raised by using radiobiological models. The purpose of this work is to evaluate the potential correlation of OAR-related toxicities to its radiobiologically estimated parameters in simultaneously integrated boost (SIB) intensity modulated radiation therapy (IMRT) plans of patients with head and neck tumors at two institutions. Lyman model for normal tissue complication probability (NTCP) and the Poisson model for tumor control probability (TCP) models were used in the Histogram Analysis in Radiation Therapy (HART) analysis. In this study, 33 patients with oropharyngeal primaries in the head and neck region were used to establish the correlation between NTCP values of (a) bilateral parotids with clinically observed rates of xerostomia, (b) esophagus with dysphagia, and (c) larynx with dysphagia. The results of the study indicated a strong correlation between the severity of xerostomia and dysphagia with Lyman NTCP of bilateral parotids and esophagus, respectively, but not with the larynx. In patients without complications, NTCP values of these organs were negligible. Using appropriate radiobiological models, the presence of a moderate to strong correlation between the severities of complications with NTCP of selected OARs suggested that the clinical outcome could be estimated prior to treatment.

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